Long-term efficacy of ultrasound-guided radiofrequency and microwave ablation for benign thyroid nodules and a nomogram to predict the regrowth
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective To assess the long-term efficacy of ultrasound-guided radiofrequency ablation (RFA) and microwave ablation (MWA) in treating benign thyroid nodules (BTNs) and to establish a nomogram predicting post-ablation regrowth.Methods This multi-center retrospective study included 611 patients with 753 BTNs treated by thermal ablation and followed for ≥24 months. Linear regression was used to identify factors affecting volume reduction rate (VRR). Univariate and multivariate Cox regression were performed to determine predictors of regrowth, which were incorporated into a predictive nomogram validated by calibration, concordance index (C-index), ROC, and decision curve analysis (DCA).Results All BTNs with a median follow-up of 36 months, nodules showed progressive shrinkage, with VRR >75% at 12 months and stabilizing near 90% by 24 months. At 60 months, the median VRR was 88.46%, with no significant difference between MWA and RFA (p > 0.05). Regrowth occurred in 6.51% (49/753) of nodules, and most were successfully retreated. Baseline maximum diameter was the only factor associated with VRR (p = 0.014). Independent predictors of regrowth included age (HR = 0.951, 95% CI: 0.909–0.994), baseline maximum diameter (HR = 1.628, 95% CI: 1.112–2.382), and energy per unit volume (HR = 0.994, 95% CI: 0.991–0.997). The nomogram demonstrated excellent performance (C-index 0.908 training, 0.792 validation), with calibration, ROC, and DCA confirming predictive accuracy and clinical utility.Conclusion RFA and MWA demonstrated durable long-term efficacy and favorable safety profiles in the treatment of BTNs, with approximately 6% of nodules developing regrowth during follow-up. A nanogram is established to predict the regrowth risk.
Authors: Yujiang Li, Lin Yan, Huacheng Yu, Na Pang, Liu Liu, Xiaoqiu Chu, Mingan Yu, Mingbo Zhang, Ying Che, Yukun Luo, Shuhang Xu
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,557
Award: G2847882193
OpenAlex ID: W4415560502
